Results 121 to 130 of about 406,624 (295)

An enhanced moth flame optimization extreme learning machines hybrid model for predicting CO2 emissions

Scientific Reports
This study introduces a novel hybrid model for accurate CO2 emissions prediction, supporting sustainable decision-making. The model integrates the Gaussian mutation and shrink mechanism-based moth flame optimization (GMSMFO) algorithm with an extreme ...
Ahmed Ramdan Almaqtouf Algwil, Wagdi M. S. Khalifa   +1 more
doaj   +1 more source

A Deep Learning Model Using Transformer Network and Expert Optimizer for an Hour Ahead Wind Power Forecasting

IEEE Access
The renewable energy platform cannot operate without an accurate wind power forecast. The power system can better manage its supply and guarantee grid reliability with an accurate wind power forecast. Accurate forecasting is difficult to achieve, though,
Anushalini Thiyagarajan, B. Sri Revathi, Vishnu Suresh   +2 more
doaj   +1 more source

Time Series Forecasts of International Tourism Demand for Australia, [PDF]

This paper examines stationary and nonstationary time series by formally testing for the presence of unit roots and seasonal unit roots prior to estimation, model selection and forecasting.
Christine Lim, Michael McAleer
core  

Carboxylic‐Acid Functionalized Multiwalled Carbon Nanotube‐Alkane‐Based Resistive Temperature Sensor for Cold Chain Applications

Advanced Engineering Materials, EarlyView.
This study presents a reversible temperature sensor with high switching ratio, ∼103. The device is fabricated using PET‐ITO and carbon nanotube dispersions in alkane. Considering its application in cold chain logistics, a proof‐of‐concept with LED is showcased. Thus, a temperature drop below the threshold temperature (crystallization temperature of the
Sunil Kumar Behera, Philippe Marrec, A. Jorge Parola, Madalena Dionísio, Hugo Águas, Andreia F. M. Santos, Hamed Pourkheirollah, Donald Lupo, César A. T. Laia   +8 more
wiley   +1 more source

Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors [PDF]

We extend the three-step generalized methods of moments (GMM) approach of Kapoor, Kelejian, and Prucha (2007), which corrects for spatially correlated errors in static panel data models, by introducing a spatial lag and a one-period lag of the dependent ...
Jacobs, J.P.A.M., Ligthart, J.E., Vrijburg, H.   +2 more
core   +1 more source

root mean square error of cross validation

Citation: 'root mean square error of cross validation' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10109 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual ...
openaire   +1 more source

Planar Solid‐State Nanopores Toward Scalable Nanofluidic Integration Based on CMOS Technology

Advanced Engineering Materials, EarlyView.
We present a scalable silicon‐based fabrication strategy for planar solid‐state nanopores to enable their integration with complex nanofluidic systems. Prototype devices demonstrate normal voltage‐current characteristics, good noise performance, and appreciable streaming currents. Our CMOS‐compatible fabrication process offers precise geometric control
Ngan Hoang Pham, Chenyu Wen, Mohammad Hadi Khaksaran, Won‐Yong Lee, Javier Cruz, Klas Hjort, Dongping Wu, Shi‐Li Zhang   +7 more
wiley   +1 more source

Comprehensive deformation study in the new Austrian tunneling technique tunnel utilising artificial neural network model

AiBi Revista de Investigación, Administración e Ingeniería
Ground deformation during tunneling projects is one of the complicated concerns that must be constantly monitored to prevent unanticipated damages and human losses.
Shubham Kanojiya, Gopal Krishna Mehta
doaj   +1 more source

A Dynamic Factor Model for the Colombian Inflation [PDF]

We use a dynamic factor model proposed by Stock and Watson [1998, 1999, 2002a,b] to forecast Colombian inflation. The model includes 92 monthly series observed over the period 1999:01-2008:06.
. Luis F. Melo, Brayan Rojas, Eliana González, Viviana Monroy   +3 more
core  

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source